Cristóbal Esteban

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 59 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #10,441 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #7,152 of 21,575 by trust here, #9,187 by citations
• Generative Models — 19 citations into it; #684 of 1,956 by trust here, #1,054 by citations
• Model Architectures — 13 citations into it
• Signal Processing — 12 citations into it; #1,875 of 7,572 by trust here, #1,958 by citations
• Natural Language Processing — 9 citations into it

Whom Cristóbal Esteban trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Sepp Hochreiter
5. Alex Graves
6. J. Urgen Schmidhuber
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Quoc V. Le
10. Yann LeCun

Who trusts Cristóbal Esteban overall

1. Eoin Brophy
2. Jinsung Yoon
3. Zhengwei Wang
4. Suman V. Ravuri
5. Anvita Gupta
6. James Jordon
7. Anne Marie Delaney
8. Laura Aviñó
9. Tomás Ward
10. Alexandre Yahi

Papers from Cristóbal Esteban's perspective

Ordered by how strongly the researchers Cristóbal Esteban trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs (2017) — 984 citations
• Early prediction of circulatory failure in the intensive care unit using machine learning (2020) — 430 citations
• Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks (2016) — 171 citations